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Dive into the world of machine learning with this course outline encompassing introduction, concept learning, decision trees, ensemble learning, neural networks, linear classifiers, support vector machines, Bayesian learning, genetic algorithms, instance-based learning, clustering, and computational learning theory. Understand the distinction between eager and lazy learners, the decision boundaries they learn, and the complexities of various learning approaches. Delve into techniques such as K-nearest neighbor, Voronoi diagrams, locally weighted regression, radial basis functions, and kernel function combinations for effective learning strategies.
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Course outline • Introduction (Ch. 1) • Concept learning (Ch. 2) • Decision trees (Ch. 3) • Ensemble learning • Neural Networks (Ch. 4) • Linear classifiers • Support Vector Machines • Bayesian Learning (Ch. 6) • Genetic Algorithms (Ch. 9) • Instance-based Learning (Ch. 8) • Clustering • Computational learning theory (Ch. 7)
Eager versus Lazy Learners • Eager learner • Learn the decision boundary at training time • Simple distributions • Lazy learners • Learn the decision boundary at testing time • High cost of classification • Can learn complex local distributions • How to define distance? • Dimensionality reduction techniques
K-nearest neighbor • Classify based on the majority label of k-nearest neighbors • Voronoi diagram defines the decision boundary for 1-nearest neighbor • Distance-weighted nearest neighbor • Possible to scale each dimension based on cross-validation
Locally weighted regression • Approximate target function by considering weighted contribution of points close to the query point • Generalization of k-nearest neighbor classification • Radial basis functions • Choose a set of kernel functions • Use their weighted combination to learn the target function • Usually Gaussian kernels • Can be achieved by a 2-layer ANN
